Exact recovery algorithm for Planted Bipartite Graph in Semi-random Graphs

TL;DR

This paper presents an efficient algorithm for exactly recovering a planted bipartite subgraph in semi-random graphs, extending previous work to smaller planted sets and utilizing a novel SDP dual construction approach.

Contribution

The paper introduces a new SDP dual construction method and provides an efficient algorithm for planted bipartite graph recovery in semi-random models, improving over prior bounds.

Findings

Recovery of planted bipartite graphs when k=Ω(√n log n)

SDP relaxation is shown to be integral via dual construction

Algorithm works under semi-random models with adversarial modifications

Abstract

The problem of finding the largest induced balanced bipartite subgraph in a given graph is NP-hard. This problem is closely related to the problem of finding the smallest Odd Cycle Transversal. In this work, we consider the following model of instances: starting with a set of vertices $V$, a set $S \subseteq V$ of $k$ vertices is chosen and an arbitrary $d$-regular bipartite graph is added on it; edges between pairs of vertices in $S \times (V \setminus S)$ and $(V \setminus S) \times (V \setminus S)$ are added with probability $p$. Since for $d=0$, the problem reduces to recovering a planted independent set, we don't expect efficient algorithms for $k=o(\sqrt{n})$. This problem is a generalization of the planted balanced biclique problem where the bipartite graph induced on $S$ is a complete bipartite graph; [Lev18] gave an algorithm for recovering $S$ in this problem when $k=\Omega(\sqrt{n})$. Our main result is an efficient algorithm that recovers (w.h.p.) the planted bipartite graph when $k=\Omega_p(\sqrt{n \log n})$ for a large range of parameters. Our results also hold for a natural semi-random model of instances, which involve the presence of a monotone adversary. Our proof shows that a natural SDP relaxation for the problem is integral by constructing an appropriate solution to it's dual formulation. Our main technical contribution is a new approach for constructing the dual solution where we calibrate the eigenvectors of the adjacency matrix to be the eigenvectors of the dual matrix. We believe that this approach may have applications to other recovery problems in semi-random models as well. When $k=\Omega(\sqrt{n})$, we give an algorithm for recovering $S$ whose running time is exponential in the number of small eigenvalues in graph induced on $S$; this algorithm is based on subspace enumeration techniques due to the works of [KT07,ABS10,Kol11].